scRNAseq

Show/hide helper functions code 
#' Make a simplified UMAP plot
#'
#' See https://twitter.com/samuel_marsh/status/1526550833479008257
#' Code https://twitter.com/timoast/status/1526237116035891200/photo/1
#'
#' @param x a ggpplot object from Seurat::DimPlot
#' @return a DimPlot with a simplified UMAP theme
#' @rdname dim_plot_mod
dim_plot_mod <- function(x) {
    p1 <- x + theme_void()
    p2 <- ggplot(data.frame(x = 100, y = 100), aes(x = x, y = y)) +
        geom_point() +
        xlim(c(0, 10)) +
        ylim(c(0, 10)) +
        theme_classic() +
        ylab("UMAP2") +
        xlab("UMAP1") +
        theme(
            axis.text.y = element_blank(),
            axis.text.x = element_blank(),
            axis.ticks = element_blank(),
            axis.line = element_line(
                arrow = arrow(length = unit(0.5, "cm"), type = "closed")
            )
        )
    layout <- c(
        patchwork::area(t = 1, l = 2, b = 11, r = 11),
        patchwork::area(t = 10, l = 1, b = 12, r = 2)
    )

    p1 + p2 + patchwork::plot_layout(design = layout)
}

#' Make a QC patchwork plot of per-sample scRNAseq data
#' - Genes per cell
#' - UMIs per cell
#' - log(UMIs per cell)
#' - % mtDNA per cell
#'
#' See https://samuel-marsh.github.io/scCustomize/articles/QC_Plots.html
#'
#' @param seurat_object a Seurat object
#' @return a patchwork plot of the QC data
#' @rdname dim_plot_mod
sc_qc_plot_per_sample <- function(seurat_object) {

    pal <- scCustomize::DiscretePalette_scCustomize(num_colors = 60, palette = "varibow")
    Seurat::Idents(seurat_object) <- "orig.ident"
    n1 <- scCustomize::QC_Plot_UMIvsGene(
        seurat_object,
        low_cutoff_gene = 800,
        high_cutoff_gene = 5500,
        low_cutoff_UMI = 500,
        high_cutoff_UMI = 50000,
        colors_use = pal,
        x_axis_label = "UMIs per Cell",
        y_axis_label = "Genes per Cell"
    ) +
        theme(legend.position = "none", axis.text.x = element_text(angle = 45, hjust = 1)) +
        labs(title = "Cells by Sample ID")
    n2 <- scCustomize::QC_Plot_UMIvsGene(
        seurat_object,
        meta_gradient_name = "percent_mt",
        low_cutoff_gene = 800,
        high_cutoff_gene = 5500,
        high_cutoff_UMI = 45000,
        x_axis_label = "UMIs per Cell",
        y_axis_label = "Genes per Cell"
    ) +
        theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
        labs(title = "Cells by % mtDNA")

    upper <- patchwork::wrap_plots(n1, n2, nrow = 1)

    p1 <- scCustomize::QC_Plots_Genes(seurat_object, low_cutoff = 800, high_cutoff = 5500, pt.size = 0, plot_title = "Genes per cell", raster = TRUE, colors_use = pal)
    p2 <- scCustomize::QC_Plots_Feature(seurat_object, feature = "percent_ribo", low_cutoff = 5, pt.size = 0, plot_title = "% rRNA per cell", raster = TRUE, colors_use = pal)
    p3 <- scCustomize::QC_Plots_UMIs(seurat_object, low_cutoff = 1200, high_cutoff = 45000, pt.size = 0, y_axis_log = TRUE, plot_title = "log(UMIs per cell)", raster = TRUE, colors_use = pal)
    p4 <- scCustomize::QC_Plots_Feature(seurat_object, feature = "percent_mt", high_cutoff = 20, pt.size = 0, plot_title = "% mtDNA per cell", raster = TRUE, colors_use = pal)

    lower <- patchwork::wrap_plots(p1, p2, p3, p4, nrow = 2) &
        ggplot2::theme(plot.title = element_text(size = 10), legend.position = "none", axis.text = element_text(size = 8))

    patchwork::wrap_plots(upper, lower, nrow = 2)
}

#' Make a QC plot of per-sample median per-cell values
#' Median values per sample:
#' - Genes per cell
#' - UMIs per cell
#' - percent mt per cell
#'
#' See https://samuel-marsh.github.io/scCustomize/articles/QC_Plots.html
#'
#' @param seurat_object a Seurat object
#' @param group_by a meta.data column with fewer than 8 unique values to group by
#' @return a patchwork plot of the QC data
#' @rdname dim_plot_mod
sc_qc_plot_group_by <- function(seurat_object, group_by) {
    pal <- scCustomize::DiscretePalette_scCustomize(num_colors = 40, palette = "ditto_seq")

    d1 <- Seurat::DimPlot(seurat_object, reduction = "umap", group.by = {{ group_by }}, cols = pal) |>
        dim_plot_mod()
    my_theme <- ggplot2::theme(plot.title = element_blank(), legend.position = "none", axis.text.y = element_text(size = 9))
    p1 <- scCustomize::Plot_Median_Genes(seurat_object, group_by = {{ group_by }}, colors_use = pal) + my_theme
    p2 <- scCustomize::Plot_Median_UMIs(seurat_object, group_by = {{ group_by }}, colors_use = pal) + my_theme
    p3 <- scCustomize::Plot_Median_Other(seurat_object, median_var = "percent_mt", group_by = {{ group_by }}, colors_use = pal) + my_theme
    p4 <- scCustomize::Plot_Median_Other(seurat_object, median_var = "percent_ribo", group_by = {{ group_by }}, colors_use = pal) + my_theme
    p5 <- scCustomize::Plot_Median_Other(seurat_object, median_var = "percent_myh11", group_by = {{ group_by }}, colors_use = pal) + my_theme
    p6 <- scCustomize::Plot_Cells_per_Sample(seurat_object, group_by = {{ group_by }}, colors_use = pal) + my_theme

    layout <- "
        AAA#G
        AAA#G
        BCDEF
        "

    patchwork::wrap_plots(A = d1, B = p1, C = p2, D = p3, E = p4, F = p5, G = p6, design = layout)
}
use_pinboard("onedrive")
seurat_object <- get_pin("mmu_10x_aml2022_GENCODEm28_HLT.rds")

Per cell metadata

seurat_object@meta.data |> colnames()
#>  [1] "orig.ident"       "nCount_RNA"       "nFeature_RNA"     "ref_genome"      
#>  [5] "tissue"           "ckit"             "percent_mt"       "percent_ribo"    
#>  [9] "percent_hb"       "percent_platelet" "percent_xist"     "chrY_counts"     
#> [13] "percent_myh11"    "nCount_SCT"       "nFeature_SCT"     "SCT_snn_res.0.4" 
#> [17] "seurat_clusters"  "SCT_snn_res.0.6"  "SCT_snn_res.0.8"  "SCT_snn_res.1"   
#> [21] "SCT_snn_res.1.2"  "SCT_snn_res.0.2"  "S.Score"          "G2M.Score"       
#> [25] "Phase"            "cell_type"        "cell_type_fine"

Number of samples: 21; total number of cells: 105170

Per sample QC metrics

sc_qc_plot_per_sample(seurat_object)

Per sample UMAP

pal <- scCustomize::DiscretePalette_scCustomize(num_colors = 60, palette = "varibow")
scCustomize::DimPlot_scCustom(seurat_object, reduction = "umap", group.by = "orig.ident", colors_use = pal) |>
    dim_plot_mod() + ggplot2::guides(color = ggplot2::guide_legend(ncol = 2))

Per cell QC metrics

sc_qc_plot_group_by(seurat_object, "tissue")

sc_qc_plot_group_by(seurat_object, "ckit")

sc_qc_plot_group_by(seurat_object, "Phase")

sc_qc_plot_group_by(seurat_object, "cell_type")

gc()
#>              used    (Mb) gc trigger    (Mb)   max used    (Mb)
#> Ncells    8616137   460.2   15947027   851.7   15947027   851.7
#> Vcells 3001119203 22896.8 4584584343 34977.7 3056506642 23319.3
use_pinboard("onedrive")
seurat_object <- get_pin("mmu_10x_mir142ko_GENCODEm28_HLT.rds")

Per cell metadata

seurat_object@meta.data |> colnames()
#>  [1] "orig.ident"       "nCount_RNA"       "nFeature_RNA"     "ref_genome"      
#>  [5] "percent_mt"       "percent_ribo"     "percent_hb"       "percent_platelet"
#>  [9] "percent_xist"     "chrY_counts"      "percent_myh11"    "nCount_SCT"      
#> [13] "nFeature_SCT"     "SCT_snn_res.0.4"  "seurat_clusters"  "SCT_snn_res.0.6" 
#> [17] "SCT_snn_res.0.8"  "SCT_snn_res.1"    "SCT_snn_res.1.2"  "SCT_snn_res.0.2" 
#> [21] "S.Score"          "G2M.Score"        "Phase"            "cell_type"       
#> [25] "cell_type_fine"

Number of samples: 18; total number of cells: 166118

Per sample QC metrics

sc_qc_plot_per_sample(seurat_object)

Per sample UMAP

pal <- scCustomize::DiscretePalette_scCustomize(num_colors = 60, palette = "varibow")
scCustomize::DimPlot_scCustom(seurat_object, reduction = "umap", group.by = "orig.ident", colors_use = pal) |>
    dim_plot_mod() + ggplot2::guides(color = ggplot2::guide_legend(ncol = 2))

Per cell QC metrics

sc_qc_plot_group_by(seurat_object, "seurat_clusters")

sc_qc_plot_group_by(seurat_object, "Phase")

sc_qc_plot_group_by(seurat_object, "cell_type")

use_pinboard("onedrive")
seurat_object <- get_pin("mmu_10x_blastcrisis_GENCODEm28_HLT.rds")

Per cell metadata

seurat_object@meta.data |> colnames()
#>  [1] "orig.ident"       "nCount_RNA"       "nFeature_RNA"     "ref_genome"      
#>  [5] "percent_mt"       "percent_ribo"     "percent_hb"       "percent_platelet"
#>  [9] "percent_xist"     "chrY_counts"      "percent_myh11"    "nCount_SCT"      
#> [13] "nFeature_SCT"     "SCT_snn_res.0.4"  "seurat_clusters"  "SCT_snn_res.0.6" 
#> [17] "SCT_snn_res.0.8"  "SCT_snn_res.1"    "SCT_snn_res.1.2"  "SCT_snn_res.0.2" 
#> [21] "S.Score"          "G2M.Score"        "Phase"            "cell_type"       
#> [25] "cell_type_fine"

Number of samples: 53; total number of cells: 310991

Per sample QC metrics

sc_qc_plot_per_sample(seurat_object)

Per sample UMAP

pal <- scCustomize::DiscretePalette_scCustomize(num_colors = 53, palette = "varibow")
scCustomize::DimPlot_scCustom(seurat_object, reduction = "umap", group.by = "orig.ident", colors_use = pal, raster = TRUE) |>
    dim_plot_mod() + ggplot2::guides(color = ggplot2::guide_legend(ncol = 2))

Per cell QC metrics

sc_qc_plot_group_by(seurat_object, "seurat_clusters")

sc_qc_plot_group_by(seurat_object, "Phase")

sc_qc_plot_group_by(seurat_object, "cell_type")

Using Python?

https://mojaveazure.github.io/seurat-disk/articles/convert-anndata.html